Estimation of Landslide and Mudslide Susceptibility with Multi-Modal Remote Sensing Data and Semantics: The Case of Yunnan Mountain Area

Fan Yang, Xiaozhi Men, Yangsheng Liu, Huigeng Mao, Yingnan Wang, Li Wang, Xiran Zhou, Chong Niu () and Xiao Xie
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Fan Yang: Shandong GEO-Surveying & Mapping Institute, Jinan 250002, China
Xiaozhi Men: Shandong GEO-Surveying & Mapping Institute, Jinan 250002, China
Yangsheng Liu: Shandong GEO-Surveying & Mapping Institute, Jinan 250002, China
Huigeng Mao: Shandong GEO-Surveying & Mapping Institute, Jinan 250002, China
Yingnan Wang: No.8 Institute of Geology and Mineral Resources Exploration of Shandong Province, Rizhao 276826, China
Li Wang: No.1 Institute of Geology and Mineral Resource Exploration of Shandong Province, Jinan 250010, China
Xiran Zhou: School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Chong Niu: Shandong GEO-Surveying & Mapping Institute, Jinan 250002, China
Xiao Xie: Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China

Land, 2023, vol. 12, issue 10, 1-15

Abstract: Landslide and mudslide susceptibility predictions play a crucial role in environmental monitoring, ecological protection, settlement planning, etc. Currently, multi-modal remote sensing data have been used for precise landslide and mudslide disaster prediction with spatial details, spectral information, or terrain attributes. However, features regarding landslide and mudslide susceptibility are often hidden in multi-modal remote sensing images, beyond the features extracted and learnt by deep learning approaches. This paper reports our efforts to conduct landslide and mudslide susceptibility prediction with multi-modal remote sensing data involving digital elevation models, optical remote sensing, and an SAR dataset. Moreover, based on the results generated by multi-modal remote sensing data, we further conducted landslide and mudslide susceptibility prediction with semantic knowledge. Through the comparisons with the ground truth datasets created by field investigation, experimental results have proved that remote sensing data can only enhance deep learning techniques to detect the landslide and mudslide, rather than the landslide and mudslide susceptibility. Knowledge regarding the potential clues about landslide and mudslide, which would be critical for estimating landslide and mudslide susceptibility, have not been comprehensively investigated yet.

Keywords: landslide and mudslide susceptibility; deep learning; multi-modal remote sensing; geospatial semantic interpretation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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